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  1. app.py +77 -0
  2. model.py +36 -0
  3. requirements.txt +3 -0
app.py ADDED
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+ ### 1. Imports and class names setup ###
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+ import gradio as gr
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+ import os
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+ import torch
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+
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+ from model import create_effnetb2_model
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+ from timeit import default_timer as timer
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+ from typing import Tuple, Dict
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+
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+ # Setup class names
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+ class_names = ["Yes","No"]
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+
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+ ### 2. Model and transforms preparation ###
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+
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+ # Create EffNetB2 model
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+ effnetb2, effnetb2_transforms = create_effnetb2_model(
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+ num_classes=2, # len(class_names) would also work
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+ )
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+
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+ # Load saved weights
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+ effnetb2.load_state_dict(
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+ torch.load(
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+ f="glass_model.pth",
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+ map_location=torch.device("cpu"), # load to CPU
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+ )
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+ )
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+
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+ ### 3. Predict function ###
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+
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+ # Create predict function
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+ def predict(img) -> Tuple[Dict, float]:
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+ """Transforms and performs a prediction on img and returns prediction and time taken.
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+ """
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+ # Start the timer
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+ start_time = timer()
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+
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+ # Transform the target image and add a batch dimension
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+ img = effnetb2_transforms(img).unsqueeze(0)
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+
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+ # Put model into evaluation mode and turn on inference mode
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+ effnetb2.eval()
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+ with torch.inference_mode():
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+ # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
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+ pred_probs = torch.softmax(effnetb2(img), dim=1)
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+
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+ # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
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+ pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
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+
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+ # Calculate the prediction time
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+ pred_time = round(timer() - start_time, 5)
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+
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+ # Return the prediction dictionary and prediction time
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+ return pred_labels_and_probs, pred_time
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+
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+ ### 4. Gradio app ###
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+
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+ # Create title, description and article strings
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+ title = "Dented car Detector"
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+ description = "An EfficientNetB2 feature extractor computer vision model to classify images of dented or good cars."
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+ article = "(https://www.learnpytorch.io/)."
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+
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+ # Create examples list from "examples/" directory
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+ example_list = [["examples/" + example] for example in os.listdir("examples")]
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+
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+ # Create the Gradio demo
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+ demo = gr.Interface(fn=predict, # mapping function from input to output
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+ inputs=gr.Image(type="pil"), # what are the inputs?
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+ outputs=[gr.Label(num_top_classes=2, label="Predictions"), # what are the outputs?
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+ gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
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+ # Create examples list from "examples/" directory
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+ examples=example_list,
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+ title=title,
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+ description=description,
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+ article=article)
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+
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+ # Launch the demo!
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+ demo.launch()
model.py ADDED
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+ import torch
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+ import torchvision
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+
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+ from torch import nn
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+
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+
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+ def create_effnetb2_model(num_classes:int=2,
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+ seed:int=42):
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+ """Creates an EfficientNetB2 feature extractor model and transforms.
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+
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+ Args:
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+ num_classes (int, optional): number of classes in the classifier head.
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+ Defaults to 3.
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+ seed (int, optional): random seed value. Defaults to 42.
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+
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+ Returns:
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+ model (torch.nn.Module): EffNetB2 feature extractor model.
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+ transforms (torchvision.transforms): EffNetB2 image transforms.
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+ """
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+ # Create EffNetB2 pretrained weights, transforms and model
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+ weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
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+ transforms = weights.transforms()
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+ model = torchvision.models.efficientnet_b2(weights=weights)
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+
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+ # Freeze all layers in base model
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+ for param in model.parameters():
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+ param.requires_grad = False
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+
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+ # Change classifier head with random seed for reproducibility
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+ torch.manual_seed(seed)
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+ model.classifier = nn.Sequential(
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+ nn.Dropout(p=0.3, inplace=True),
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+ nn.Linear(in_features=1408, out_features=num_classes),
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+ )
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+
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+ return model, transforms
requirements.txt ADDED
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+ torch==1.12.0
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+ torchvision==0.13.0
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+ gradio==3.1.4